A compact embedding specialist that punches above its weight class at 350M parameters. It converts text into dense vector representations across a generous 128K token context window, making it capable of encoding long documents without truncation. Its small footprint makes it practical for deployment in resource-constrained environments where larger embedding models would be impractical.